Background Emergency Departments (EDs) have become a central point of contact for diverse populations seeking services to treat primary care medical concerns and manage chronic conditions. In addition, regularly seeking ED treatment for primary care concerns prevents the development of a substantive relationship with a medical home. A medical home delivers continuity in care that may result in better management of chronic disease and conditions and, thus, contribute to a decrease in suffering and an increase in longevity. Aims This project will model and simulate the flow of populations with low-acuity conditions among various treatment venues within the Hampton Roads region and test the sensitivity of competing interventions in the re-direction of these patients away from the Emergency Department and towards treatment venues which may offer increased continuity in care to the patient. Directing patients to more appropriate treatment homes is a recognized policy resistant problem. Approach Traditional approaches have not been well suited to anticipate the second and third order consequences of interventions upon the larger healthcare system and, in some instance, have resulted in policy resistance. Our System Dynamics approach will lead us to understand the dynamic demand-capacity behavior of the regional health system over time and may empirically demonstrate changes in the behavior of the system that are counterintuitive or would not have been evident if approached with more traditional causal methodologies. Significance We are applying a specific system science methodology to a public policy resistant health care system problem that is behavioral and social in nature. While this research draws upon one of this nation's large metropolitan regions, we offer the development of a general model that may be extended to the dynamics of other regions that, invariably, maintain different service capacity among their treatment venues. With our proposed approach, decision makers may simulate over time the dynamic interaction and 'ripple effect' of adopting any combination of interventions and the sensitivity of various sub-populations, referred to as 'silos,' to these interventions. This research will enhance both public and private decision making in the prioritization of interventions within a system where resources are limited, adoption of interventions may be met with skepticism or delay, and the interventions themselves may result in policy resistance.